This study intends to examine the amount of caffeine in energy drinks, specifically sting, predator, Orello hydra , monster and evaluate any possible negative impacts on students who consume caffeinated energy drinks, such as a variety of health problem. The use of caffeinated energy drinks disrupts students sleep cycle and shortens their sleep length. Furthermore, studies have shown that children that ingest caffeinated have much greater blood sugar levels. This rise in the body’s toxicity may have negative effects. There is a connection between student deaths and the high caffeine level of caffeinated energy drinks. The beverages high sugar and caffeine content is the main cause of this. The study report also looks into several analytical techniques for figuring out how much caffeine is present in various body tissues There is also discussion about the negative effects of caffeine on the human body , particularly for those who drink it regularly. Caffeine addicts have been shown to have toxic side effects, such as elevated heart rate , restlessness, and anxiety. The study examines a number of analytical methods, such as UV-visible spectroscopy, TLC, and light microscope, to quantify the amount of caffeine. The techniques to effectively ascertain the caffeine content of an energy drink are provided in a step-by-step order. Overall, this study emphasizes the possible risks associated with ingesting energy drinks strong in caffeine, such as sting, especially for students. It is possible to limit caffeine consumption and lower health risks by being aware of the negative effects and putting good analytical procedures.
{"title":"Analysis of Caffeine Content in Energy Drink and Its Impact on Students Health (by using UV-Visible Spectroscopy, HPTLC and Microscopy)","authors":"Veena Kamble, Abhishekh Patil, Gaurav Varade","doi":"10.32628/ijsrst2411328","DOIUrl":"https://doi.org/10.32628/ijsrst2411328","url":null,"abstract":"This study intends to examine the amount of caffeine in energy drinks, specifically sting, predator, Orello hydra , monster and evaluate any possible negative impacts on students who consume caffeinated energy drinks, such as a variety of health problem. The use of caffeinated energy drinks disrupts students sleep cycle and shortens their sleep length. Furthermore, studies have shown that children that ingest caffeinated have much greater blood sugar levels. This rise in the body’s toxicity may have negative effects. There is a connection between student deaths and the high caffeine level of caffeinated energy drinks. The beverages high sugar and caffeine content is the main cause of this. The study report also looks into several analytical techniques for figuring out how much caffeine is present in various body tissues There is also discussion about the negative effects of caffeine on the human body , particularly for those who drink it regularly. Caffeine addicts have been shown to have toxic side effects, such as elevated heart rate , restlessness, and anxiety. The study examines a number of analytical methods, such as UV-visible spectroscopy, TLC, and light microscope, to quantify the amount of caffeine. The techniques to effectively ascertain the caffeine content of an energy drink are provided in a step-by-step order. Overall, this study emphasizes the possible risks associated with ingesting energy drinks strong in caffeine, such as sting, especially for students. It is possible to limit caffeine consumption and lower health risks by being aware of the negative effects and putting good analytical procedures.","PeriodicalId":14387,"journal":{"name":"International Journal of Scientific Research in Science and Technology","volume":" 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141368958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Multi-span simply supported (MSSS) bridges are extensively utilized worldwide, yet they often suffer damage or failure during earthquakes. This study employs three-dimensional bridge analysis and design software, CSiBridge, to analyze the seismic response of bridge. The investigation focuses on an existing concrete bridge subjected to the El Centro earthquake ground acceleration. Three distinct underlying soil conditions are considered to assess soil-structure interaction (SSI). Analysis of pier top displacements reveals that bridges with linear horizontal layouts may exhibit minimal displacements and limited impact from soil-structure interaction. However, configurations with irregular horizontal layouts result in notable displacements. This research sheds light on the dynamic behavior of bridges and emphasizes the significance of considering geometric variations in seismic analysis.
多跨简支（MSSS）桥梁在全球范围内得到广泛应用，但在地震中却经常遭受破坏或失效。本研究采用三维桥梁分析和设计软件 CSiBridge 分析桥梁的地震响应。调查的重点是一座承受 El Centro 地震地面加速度的现有混凝土桥梁。考虑了三种不同的基础土壤条件，以评估土壤-结构相互作用（SSI）。墩顶位移分析表明，线性水平布局的桥梁位移最小，土-结构相互作用的影响有限。然而，不规则水平布局的结构会产生显著的位移。这项研究揭示了桥梁的动态行为，并强调了在地震分析中考虑几何变化的重要性。
{"title":"Seismic Performance Analysis of Multi-Span Simply Supported Bridges","authors":"John Sonam, Talkeshwar Ray","doi":"10.32628/ijsrst2411333","DOIUrl":"https://doi.org/10.32628/ijsrst2411333","url":null,"abstract":"Multi-span simply supported (MSSS) bridges are extensively utilized worldwide, yet they often suffer damage or failure during earthquakes. This study employs three-dimensional bridge analysis and design software, CSiBridge, to analyze the seismic response of bridge. The investigation focuses on an existing concrete bridge subjected to the El Centro earthquake ground acceleration. Three distinct underlying soil conditions are considered to assess soil-structure interaction (SSI). Analysis of pier top displacements reveals that bridges with linear horizontal layouts may exhibit minimal displacements and limited impact from soil-structure interaction. However, configurations with irregular horizontal layouts result in notable displacements. This research sheds light on the dynamic behavior of bridges and emphasizes the significance of considering geometric variations in seismic analysis.","PeriodicalId":14387,"journal":{"name":"International Journal of Scientific Research in Science and Technology","volume":" 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141372201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Charging systems from shore to ship are typically designed based on a range of operational and design parameters, encompassing onboard power and propulsion needs, available charging durations, and the capacity of local power networks. In areas with weak grid infrastructure, onshore energy storage is often employed to facilitate high-power charging for vessels requiring short charging intervals. Nevertheless, incorporating on-shore energy storage adds complexity to the system, and the selection of system configuration can profoundly affect the efficiency of energy transfer from the grid to the vessel. This study presents a comparative analysis of energy efficiency among AC, DC, and Inductive shore-to-ship charging solutions for short-distance ferries utilizing both AC and DC-based propulsion systems. Findings illustrate that an increased reliance on onshore battery power correlates with decreased overall energy efficiency during the charging process. Thus, optimizing energy efficiency necessitates careful consideration when distributing the load between the grid and onshore battery. Results indicate that DC charging offers advantages over other solutions for AC-based propulsion systems in terms of energy efficiency. However, for DC-based propulsion systems, the most efficient solution may be either DC or AC charging, contingent upon the distribution of load between the grid and onshore battery. Furthermore, it is inferred that despite adding additional conversion stages and complexity to the system, the energy efficiency of inductive charging is comparable to wired schemes. Considering the added benefits of contactless charging, such as reliability, safety, and robustness, these findings advocate for the adoption of inductive charging as a promising solution.
{"title":"Assessment of the Effectiveness of Energy Transfer for Shore-to-Ship Fast Charging Systems","authors":"Kulat Hemat Sahebrao","doi":"10.32628/ijsrst24113131","DOIUrl":"https://doi.org/10.32628/ijsrst24113131","url":null,"abstract":"Charging systems from shore to ship are typically designed based on a range of operational and design parameters, encompassing onboard power and propulsion needs, available charging durations, and the capacity of local power networks. In areas with weak grid infrastructure, onshore energy storage is often employed to facilitate high-power charging for vessels requiring short charging intervals. Nevertheless, incorporating on-shore energy storage adds complexity to the system, and the selection of system configuration can profoundly affect the efficiency of energy transfer from the grid to the vessel. This study presents a comparative analysis of energy efficiency among AC, DC, and Inductive shore-to-ship charging solutions for short-distance ferries utilizing both AC and DC-based propulsion systems. Findings illustrate that an increased reliance on onshore battery power correlates with decreased overall energy efficiency during the charging process. Thus, optimizing energy efficiency necessitates careful consideration when distributing the load between the grid and onshore battery. Results indicate that DC charging offers advantages over other solutions for AC-based propulsion systems in terms of energy efficiency. However, for DC-based propulsion systems, the most efficient solution may be either DC or AC charging, contingent upon the distribution of load between the grid and onshore battery. Furthermore, it is inferred that despite adding additional conversion stages and complexity to the system, the energy efficiency of inductive charging is comparable to wired schemes. Considering the added benefits of contactless charging, such as reliability, safety, and robustness, these findings advocate for the adoption of inductive charging as a promising solution.","PeriodicalId":14387,"journal":{"name":"International Journal of Scientific Research in Science and Technology","volume":" 89","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141374956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
White mulberry leaves (Morus alba L.) contain flavonoid which empirically have many health benefits, such as reducing fever, relieving headaches and throats, coughs, dysentery, digestive disorders, flu, and according to research it has antioxidant activity. Many factors effect the total flavonoid content, one of which is the concentration of the extraction solvent. The purpose of the research was to determine the effect of different ethanol concentrations as the extraction solvent on total flavonoid content of white mulberry (Morus alba L.) leaves extract. White mulberry leaves (Morus alba L.) were extracted using the maceration method, the extract was tested for phytochemical screening and thin layer chromatography using chloroform: ethyl acetate (6: 4) as the eluent. The resulting spots were observed visually and with λ 254 nm and λ 366 nm UV light. Determination of flavonoid content by UV-Vis spectrophotometry using the AlCl3 colorimetric method at a maximum wavelength of 415 nm. The research results showed that the yield of 50% and 96% ethanol extract of white mulberry leaves (Morus alba L.) was 17.42 % and 11.04 %. Phytochemical screening showed that both extracts positively contained flavonoids. The presence of flavonoids was confirmed using a thin layer chromatography test, resulting in an average Rf value for 50% and 96% ethanol extracts are 0.71 and 0.70, close to quercetin Rf standard is 0,73 as comparison. The total flavonoid content obtained from the 50% ethanol extract was 36.0902 mgQE/g and the 96% ethanol extract was 56.4699 mgQE/g. The conclusions are the concentration of the extraction solvent effect the yield and total flavonoid content.
{"title":"The Effect of Different Ethanol Concentrations as The Extraction Solvent on Total Flavonoid Content of White Mulberry Leaves Extracts (Morus alba L.)","authors":"Harpolia Cartika, Khairun Nida, Ilma Atania Ahya","doi":"10.32628/ijsrst2411320","DOIUrl":"https://doi.org/10.32628/ijsrst2411320","url":null,"abstract":"White mulberry leaves (Morus alba L.) contain flavonoid which empirically have many health benefits, such as reducing fever, relieving headaches and throats, coughs, dysentery, digestive disorders, flu, and according to research it has antioxidant activity. Many factors effect the total flavonoid content, one of which is the concentration of the extraction solvent. The purpose of the research was to determine the effect of different ethanol concentrations as the extraction solvent on total flavonoid content of white mulberry (Morus alba L.) leaves extract. White mulberry leaves (Morus alba L.) were extracted using the maceration method, the extract was tested for phytochemical screening and thin layer chromatography using chloroform: ethyl acetate (6: 4) as the eluent. The resulting spots were observed visually and with λ 254 nm and λ 366 nm UV light. Determination of flavonoid content by UV-Vis spectrophotometry using the AlCl3 colorimetric method at a maximum wavelength of 415 nm. The research results showed that the yield of 50% and 96% ethanol extract of white mulberry leaves (Morus alba L.) was 17.42 % and 11.04 %. Phytochemical screening showed that both extracts positively contained flavonoids. The presence of flavonoids was confirmed using a thin layer chromatography test, resulting in an average Rf value for 50% and 96% ethanol extracts are 0.71 and 0.70, close to quercetin Rf standard is 0,73 as comparison. The total flavonoid content obtained from the 50% ethanol extract was 36.0902 mgQE/g and the 96% ethanol extract was 56.4699 mgQE/g. The conclusions are the concentration of the extraction solvent effect the yield and total flavonoid content.","PeriodicalId":14387,"journal":{"name":"International Journal of Scientific Research in Science and Technology","volume":"303 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141386297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study investigates awareness levels in mutual fund investment with special reference to Kottayam District. The sample size of 50 respondents are collected from mutual fund investors with different demographic back grounds by using a scheduled questionnaire. This study explores demographic factors of mutual fund investors in Kottayam and study the investors awareness regarding mutual fund investment. This study reveals that male respondents from rural areas of Kottayam with 50 years and above age having post-graduation invest more money in mutual funds than all other respondents. The awareness about the different aspects of mutual fund investment is not enough to make a successful investment strategy to the investors from Kottayam. By conducting awareness programs and financial literacy programs can improve their knowledge in mutual fund investment.
{"title":"A Study on Awareness of Mutual Fund Investment (With Special Reference to Kottayam District)","authors":"Susan Bincy Andrews, Dr. Rafeekamol C. A","doi":"10.32628/ijsrst2411325","DOIUrl":"https://doi.org/10.32628/ijsrst2411325","url":null,"abstract":"This study investigates awareness levels in mutual fund investment with special reference to Kottayam District. The sample size of 50 respondents are collected from mutual fund investors with different demographic back grounds by using a scheduled questionnaire. This study explores demographic factors of mutual fund investors in Kottayam and study the investors awareness regarding mutual fund investment. This study reveals that male respondents from rural areas of Kottayam with 50 years and above age having post-graduation invest more money in mutual funds than all other respondents. The awareness about the different aspects of mutual fund investment is not enough to make a successful investment strategy to the investors from Kottayam. By conducting awareness programs and financial literacy programs can improve their knowledge in mutual fund investment.","PeriodicalId":14387,"journal":{"name":"International Journal of Scientific Research in Science and Technology","volume":"58 28","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141383895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The UGC guidelines for institutional development plans for higher education institutions empowering the faculty is the critical pillar. Extending from purpose-informed recruitment processes to a culture that encourages continuous professional development. Faculty diversity is valued, nurturing talent, and offers pathways for career progression in line with and extending beyond global best practices. The eight enabler parameters are enumerated in the present context. Enablers of Governance, Financial, to Academic, Research & Intellectual Property, Networking & Collaboration, Human Resources, Supportive & Facilitative, Physical and Digital.
{"title":"UGC Proposed Parameters for Institutional Excellence in IDP Framework","authors":"Dr. H. M. Naveen","doi":"10.32628/ijsrst24113126","DOIUrl":"https://doi.org/10.32628/ijsrst24113126","url":null,"abstract":"The UGC guidelines for institutional development plans for higher education institutions empowering the faculty is the critical pillar. Extending from purpose-informed recruitment processes to a culture that encourages continuous professional development. Faculty diversity is valued, nurturing talent, and offers pathways for career progression in line with and extending beyond global best practices. The eight enabler parameters are enumerated in the present context. Enablers of Governance, Financial, to Academic, Research & Intellectual Property, Networking & Collaboration, Human Resources, Supportive & Facilitative, Physical and Digital.","PeriodicalId":14387,"journal":{"name":"International Journal of Scientific Research in Science and Technology","volume":"52 s35","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141382996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Speech Emotion Recognition (SER) is a Machine Learning (ML) topic that has attracted substantial attention from researchers, particularly in the field of emotional computing. This is because of its growing potential, improvements in algorithms, and real-world applications. Pitch, intensity, and Mel-Frequency Cepstral Coefficients (MFCC) are examples of quantitative variables that can be used to represent the paralinguistic information found in human speech. The three main processes of data processing, feature selection/extraction, and classification based on the underlying emotional traits are typically followed to achieve SER. The use of ML techniques for SER implementation is supported by the nature of these processes as well as the unique characteristics of human speech. Several ML techniques were used in recent affective computing research projects for SER tasks; Only a few number of them, nevertheless, adequately convey the fundamental strategies and tactics that can be applied to support the three essential phases of SER implementation. Additionally, these works either overlook or just briefly explain the difficulties involved in completing these tasks and the cutting-edge methods employed to overcome them. With a focus on the three SER implementation processes, we give a comprehensive assessment of research conducted over the past ten years that tackled SER challenges from machine learning perspectives in this study. A number of difficulties are covered in detail, including the problem of Speaker-Independent experiments' low classification accuracy and related solutions. The review offers principles for SER evaluation as well, emphasizing indicators that can be experimented with and common baselines. The purpose of this paper is to serve as a a thorough manual that SER researchers may use to build SER solutions using ML techniques, inspire potential upgrades to current SER models, or spark the development of new methods to improve SER performance.
语音情感识别（SER）是一个机器学习（ML）课题，吸引了大量研究人员的关注，尤其是在情感计算领域。这是因为其潜力不断增长、算法不断改进以及在现实世界中的应用。音高、音强和梅尔频率倒频谱系数（MFCC）是量化变量的示例，可用来表示人类语音中的副语言信息。要实现 SER，通常需要经过数据处理、特征选择/提取和基于基本情感特征的分类这三个主要过程。这些过程的性质以及人类语音的独特特征都支持使用 ML 技术来实现 SER。在最近的情感计算研究项目中，有几种 ML 技术被用于 SER 任务；然而，其中只有少数几种技术充分传达了可用于支持 SER 实施的三个基本阶段的基本战略和策略。此外，这些著作要么忽略了完成这些任务所涉及的困难，要么只是简要说明了克服这些困难所采用的前沿方法。在本研究中，我们以三个 SER 实施过程为重点，对过去十年间从机器学习角度应对 SER 挑战的研究进行了全面评估。其中详细介绍了一些难题，包括与说话者无关的实验分类准确率低的问题及相关解决方案。综述还提供了 SER 评估的原则，强调了可进行实验的指标和通用基线。本文旨在提供一本详尽的手册，供 SER 研究人员使用 ML 技术构建 SER 解决方案，激发对当前 SER 模型的潜在升级，或激发开发新方法以提高 SER 性能。
{"title":"An Enhanced Human Speech Based Emotion Recognition","authors":"Dr. M. Narendra, Lankala Suvarchala","doi":"10.32628/ijsrst24113128","DOIUrl":"https://doi.org/10.32628/ijsrst24113128","url":null,"abstract":"Speech Emotion Recognition (SER) is a Machine Learning (ML) topic that has attracted substantial attention from researchers, particularly in the field of emotional computing. This is because of its growing potential, improvements in algorithms, and real-world applications. Pitch, intensity, and Mel-Frequency Cepstral Coefficients (MFCC) are examples of quantitative variables that can be used to represent the paralinguistic information found in human speech. The three main processes of data processing, feature selection/extraction, and classification based on the underlying emotional traits are typically followed to achieve SER. The use of ML techniques for SER implementation is supported by the nature of these processes as well as the unique characteristics of human speech. Several ML techniques were used in recent affective computing research projects for SER tasks; Only a few number of them, nevertheless, adequately convey the fundamental strategies and tactics that can be applied to support the three essential phases of SER implementation. Additionally, these works either overlook or just briefly explain the difficulties involved in completing these tasks and the cutting-edge methods employed to overcome them. With a focus on the three SER implementation processes, we give a comprehensive assessment of research conducted over the past ten years that tackled SER challenges from machine learning perspectives in this study. A number of difficulties are covered in detail, including the problem of Speaker-Independent experiments' low classification accuracy and related solutions. The review offers principles for SER evaluation as well, emphasizing indicators that can be experimented with and common baselines. The purpose of this paper is to serve as a a thorough manual that SER researchers may use to build SER solutions using ML techniques, inspire potential upgrades to current SER models, or spark the development of new methods to improve SER performance.","PeriodicalId":14387,"journal":{"name":"International Journal of Scientific Research in Science and Technology","volume":"51 34","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141383866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Idling buses in bus station micro-environments potentially pollute ambient air with health implications for commuters with long waiting times. This study aimed at finding the severity of air pollutants in selected three bus station microenvironments in Enugu metropolis, South Eastern Nigeria with the objective to determine the air quality index. Real time measurements of ambient particulates (PM10 and PM2.5) and gaseous pollutants such as SO2, NO2 and CO were monitored, in conjunction with meteorological parameters (wind speed, temperature and relative humidity) on idling buses in selected three bus stations and a control station in Enugu Metropolis. The study spanned through September 2016 to August 2018, covering dry and wet seasons. Hand-held Aeroqual Series - 200 gas monitor/sensors, particulates laser meter and ambient weather anemometer were used to collect data. Results indicated that the daily highest mean concentrations of pollutants were as follows: PM10=228µg/m3 – Young Shall Grow; PM2.5=99.5µg/m3 – Young-Shall-Grow; SO2=0.81ppm – Ifesinachi; NO2= 0.068ppm – Young-Shall-Grow; CO= 7.376ppm – ABC, consistently maintained higher concentrations than those at the control station. AQI was calculated with AQI calculator. The air quality index (AQI) results showed a severe health issue in the selected 3-bus station micro environments. This implies that commuters are highly at risk if they cluster idling buses at bus stations. This study can assist the relevant authorities to set strict ambient air quality objectives for pollutants in transport micro environments.
{"title":"Pollutants In the Selected 3 – Bus Station Microenvironments in Enugu Metropolis","authors":"Ehiemobi Michael Chijioke","doi":"10.32628/ijsrst24113110","DOIUrl":"https://doi.org/10.32628/ijsrst24113110","url":null,"abstract":"Idling buses in bus station micro-environments potentially pollute ambient air with health implications for commuters with long waiting times. This study aimed at finding the severity of air pollutants in selected three bus station microenvironments in Enugu metropolis, South Eastern Nigeria with the objective to determine the air quality index. Real time measurements of ambient particulates (PM10 and PM2.5) and gaseous pollutants such as SO2, NO2 and CO were monitored, in conjunction with meteorological parameters (wind speed, temperature and relative humidity) on idling buses in selected three bus stations and a control station in Enugu Metropolis. The study spanned through September 2016 to August 2018, covering dry and wet seasons. Hand-held Aeroqual Series - 200 gas monitor/sensors, particulates laser meter and ambient weather anemometer were used to collect data. Results indicated that the daily highest mean concentrations of pollutants were as follows: PM10=228µg/m3 – Young Shall Grow; PM2.5=99.5µg/m3 – Young-Shall-Grow; SO2=0.81ppm – Ifesinachi; NO2= 0.068ppm – Young-Shall-Grow; CO= 7.376ppm – ABC, consistently maintained higher concentrations than those at the control station. AQI was calculated with AQI calculator. The air quality index (AQI) results showed a severe health issue in the selected 3-bus station micro environments. This implies that commuters are highly at risk if they cluster idling buses at bus stations. This study can assist the relevant authorities to set strict ambient air quality objectives for pollutants in transport micro environments.","PeriodicalId":14387,"journal":{"name":"International Journal of Scientific Research in Science and Technology","volume":"68 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141109926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this era cancer is biggest problem for human resource and there is multiple type of cancer like blood cancer, breast cancer etc. but here we are talk about only breast cancer. Breast cancer is generally faced by women. Every year the cases of breast cancer increase rapidly and as well as death rate also. The breast cancer is the cause of death in women worldwide. To make breast cancer prediction model is a very challenging task because high accuracy in prediction is very important to know the actual condition or results in the form of chances of survival. Through Machine Learning the prediction and early diagnosis can be easy by using different techniques and algorithms of Machine Learning. But here we use classification method to predict the breast cancer.
{"title":"Breast Cancer Prediction Web Model","authors":"Ashish Chauhan, Kajal Kori, Priti Pal, Khushi Sani","doi":"10.32628/ijsrst24113102","DOIUrl":"https://doi.org/10.32628/ijsrst24113102","url":null,"abstract":"In this era cancer is biggest problem for human resource and there is multiple type of cancer like blood cancer, breast cancer etc. but here we are talk about only breast cancer. Breast cancer is generally faced by women. Every year the cases of breast cancer increase rapidly and as well as death rate also. The breast cancer is the cause of death in women worldwide. To make breast cancer prediction model is a very challenging task because high accuracy in prediction is very important to know the actual condition or results in the form of chances of survival. Through Machine Learning the prediction and early diagnosis can be easy by using different techniques and algorithms of Machine Learning. But here we use classification method to predict the breast cancer.","PeriodicalId":14387,"journal":{"name":"International Journal of Scientific Research in Science and Technology","volume":"97 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141122306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Startup AYUSH Portal is a pioneering initiative to transform the AYUSH ecosystem. It acts as a key hub and brings together startups, investors, incubators, accelerators, government agencies and the public. This innovative platform fosters collaboration, knowledge sharing, and growth in integrated healthcare. With the growing global interest in holistic medical and wellness practices, this platform serves as a dynamic and comprehensive hub for all stakeholders in the AYUSH ecosystem. The core of the portal is to facilitate seamless interaction and collaboration between startups, investors, incubators, accelerators, government agencies and the public. It provides a user-friendly interface that allows startups to showcase innovative AYUSH-related products and services, while investors explore investment opportunities in growing sectors. AYUSH Startup Portal is a key solution that will revolutionize the AYUSH startup industry. Startups can benefit from shared workspaces where they can showcase their AYUSH-related products and services. Investors have access to a rich pool of investment opportunities. Mentorship programs empower startups under the guidance of seasoned professionals. Our extensive resource center provides important information, regulatory updates and financing options. Key to our success are a variety of features, including collaborative workspaces, mentoring opportunities, abundant resources, and a global networking environment. Users can access the latest regulatory information, participate in virtual events and webinars, and engage with expert mentors. Government agencies can disseminate important policy updates by creating a regulatory environment conducive to AYUSH innovation. In a world where holistic health and wellness is gaining momentum, the AYUSH Startup Portal is a catalyst for advancement, unity and knowledge sharing within the AYUSH startup community, ultimately contributing to the growth of this important sector.
{"title":"Startup-AYUSH Portal","authors":"Chandra Sekhar Chauhan, Shailendra Chaurasiya, Harshvardhan Tiwary, Mahesh Yadav","doi":"10.32628/ijsrst24113109","DOIUrl":"https://doi.org/10.32628/ijsrst24113109","url":null,"abstract":"The Startup AYUSH Portal is a pioneering initiative to transform the AYUSH ecosystem. It acts as a key hub and brings together startups, investors, incubators, accelerators, government agencies and the public. This innovative platform fosters collaboration, knowledge sharing, and growth in integrated healthcare. With the growing global interest in holistic medical and wellness practices, this platform serves as a dynamic and comprehensive hub for all stakeholders in the AYUSH ecosystem. The core of the portal is to facilitate seamless interaction and collaboration between startups, investors, incubators, accelerators, government agencies and the public. It provides a user-friendly interface that allows startups to showcase innovative AYUSH-related products and services, while investors explore investment opportunities in growing sectors. AYUSH Startup Portal is a key solution that will revolutionize the AYUSH startup industry. Startups can benefit from shared workspaces where they can showcase their AYUSH-related products and services. Investors have access to a rich pool of investment opportunities. Mentorship programs empower startups under the guidance of seasoned professionals. Our extensive resource center provides important information, regulatory updates and financing options. Key to our success are a variety of features, including collaborative workspaces, mentoring opportunities, abundant resources, and a global networking environment. Users can access the latest regulatory information, participate in virtual events and webinars, and engage with expert mentors. Government agencies can disseminate important policy updates by creating a regulatory environment conducive to AYUSH innovation. In a world where holistic health and wellness is gaining momentum, the AYUSH Startup Portal is a catalyst for advancement, unity and knowledge sharing within the AYUSH startup community, ultimately contributing to the growth of this important sector.","PeriodicalId":14387,"journal":{"name":"International Journal of Scientific Research in Science and Technology","volume":"13 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141119671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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